Identification of potential crucial genes associated with early-onset preeclampsia via bioinformatic analysis

Detection and assessment of immune and stromal related risk genes to predict preeclampsia: A bioinformatics analysis with dataset

This study aimed to investigate immune score and stromal score-related signatures associated with preeclampsia (PE) and identify key genes for diagnosing PE using bioinformatics analysis. Four microarray datasets, GSE75010, GSE25906, GSE44711, and GSE10588 were obtained from the Gene Expression Omnibus database. GSE75010 was utilized for differential expressed gene (DEGs) analysis. Subsequently, bioinformatic tools such as gene ontology, Kyoto Encyclopedia of Genes and Genomes, weighted gene correlation network analysis, and gene set enrichment analysis were employed to functionally characterize candidate target genes involved in the pathogenesis of PE. The least absolute shrinkage and selection operator regression approach was employed to identify crucial genes and develop a predictive model. This method also facilitated the creation of receiver operating characteristic (ROC) curves, enabling the evaluation of the model's precision. Furthermore, the model underwent external validation through the other three datasets. A total of 3286 DEGs were identified between normal and PE tissues. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed enrichments in functions related to cell chemotaxis, cytokine binding, and cytokine-cytokine receptor interaction. weighted gene correlation network analysis identified 2 color modules strongly correlated with immune and stromal scores. After intersecting DEGs with immune and stromal-related genes, 13 genes were selected and added to the least absolute shrinkage and selection operator regression. Ultimately, 7 genes were screened out to establish the risk model for discriminating preeclampsia from controls, with each gene having an area under the ROC curve >0.70. The constructed risk model demonstrated that the area under the ROC curves in internal and the other three external datasets were all greater than 0.80. A 7-gene risk signature was identified to build a potential diagnostic model and performed well in the external validation group for PE patients. These findings illustrated that immune and stromal cells played essential roles in PE during its progression.

Comparison of immune-related gene signatures and immune infiltration features in early- and late-onset preeclampsia

BACKGROUND

Preeclampsia, a severe pregnancy syndrome, is widely accepted divided into early- and late-onset preeclampsia (EOPE and LOPE) based on the onset time of preeclampsia, with distinct pathophysiological origins. However, the molecular mechanism especially immune-related mechanisms for EOPE and LOPE is currently obscure. In the present study, we focused on placental immune alterations between EOPE and LOPE and search for immune-related biomarkers that could potentially serve as potential therapeutic targets through bioinformatic analysis.

METHODS

The gene expression profiling data was obtained from the Gene Expression Omnibus database. ESTIMATE algorithm and Gene Set Enrichment Analysis were employed to evaluate the immune status. The intersection of differentially expressed genes in GSE74341 series and immune-related genes set screened differentially expressed immune-related genes. Protein-protein interaction network and random forest were used to identify hub genes with a validation by a quantitative real-time PCR. Kyoto Encyclopedia of Genes and Genomes pathways, Gene Ontology and gene set variation analysis were utilized to conduct biological function and pathway enrichment analyses. Single-sample gene set enrichment analysis and CIBERSORTx tools were employed to calculate the immune cell infiltration score. Correlation analyses were evaluated by Pearson correlation analysis. Hub genes-miRNA network was performed by the NetworkAnalyst online tool.

RESULTS

Immune score and stromal score were all lower in EOPE samples. The immune system-related gene set was significantly downregulated in EOPE compared to LOPE samples. Four hub differentially expressed immune-related genes (IL15, GZMB, IL1B and CXCL12) were identified based on a protein-protein interaction network and random forest. Quantitative real-time polymerase chain reaction validated the lower expression levels of four hub genes in EOPE compared to LOPE samples. Immune cell infiltration analysis found that innate and adaptive immune cells were apparent lacking in EOPE samples compared to LOPE samples. Cytokine-cytokine receptor, para-inflammation, major histocompatibility complex class I and T cell co-stimulation pathways were significantly deficient and highly correlated with hub genes. We constructed a hub genes-miRNA regulatory network, revealing the correlation between hub genes and hsa-miR-374a-5p, hsa-miR-203a-3p, hsa-miR-128-3p, hsa-miR-155-3p, hsa-miR-129-2-3p and hsa-miR-7-5p.

CONCLUSIONS

The innate and adaptive immune systems were severely impaired in placentas of EOPE. Four immune-related genes (IL15, GZMB, IL1B and CXCL12) were closely correlated with immune-related pathogenesis of EOPE. The result of our study may provide a new basis for discriminating between EOPE and LOPE and acknowledging the role of the immune landscape in the eventual interference and tailored treatment of EOPE.

Serum protein profile analysis via label-free quantitation proteomics in patients with early-onset preeclampsia

BACKGROUND

Preeclampsia (PE) is a serious pregnancy complication, resulting in potentially life-threatening conditions for both mother and foetus. It is worth noting that early-onset PE has become a great challenge for clinicians due to its complex manifestation, rapid progression and serious complications. This study aims to investigate differential serum proteome profiles in patients with early-onset PE.

METHODS

Each serum sample was separated using a nanoliter flow rate Easy-nLC chromatography system. Then the samples were analysed by mass spectrometry. Bioinformatics analyses were conducted to analyse the functional categories or signal transduction pathways for differentially abundant proteins. Key proteins identified by mass spectrometry were verified by ELISA.

RESULTS

We found 30 and 34 proteins were upregulated and downregulated in early-onset PE patients (n = 3) vs controls (n = 3), respectively. Functional enrichment analysis revealed differentially expressed proteins related to the immune response and regulation of peptidase activity. ELISA confirmed that there were lower CSH1 levels and higher LPA concentrations in the serum samples of early-onset PE patients (n = 22) than in healthy controls (n = 19) (p < 0.05 for CSH1 and p < 0.001 for LPA).

CONCLUSIONS

This study revealed the critical features of serum proteins in early-onset PE patients. LPA and CSH1 may serve as biomarkers for early-onset PE diagnosis and therapy.

Association of the LEP gene with immune infiltration as a diagnostic biomarker in preeclampsia

Objective: Preeclampsia (PE) is a serious condition in pregnant women and hence an important topic in obstetrics. The current research aimed to recognize the potential and significant immune-related diagnostic biomarkers for PE. Methods: From the Gene Expression Omnibus (GEO) data sets, three public gene expression profiles (GSE24129, GSE54618, and GSE60438) from the placental samples of PE and normotensive pregnancy were downloaded. Differentially expressed genes (DEGs) were selected and determined among 73 PE and 85 normotensive control pregnancy samples. The DEGs were used for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA). The candidate biomarkers were identified by the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analysis. The receiver operating characteristic curve (ROC) was applied to evaluate diagnostic ability. For further confirmation, the expression levels and diagnostic value of biomarkers in PE were verified in the GSE75010 data set (80 PE and 77 controls) and validated by qRT-RCR, Western blot, and immunohistochemistry (IHC). The CIBERSORT algorithm was used to calculate the compositional patterns of 22 types of immune cells in PE. Results: In total, 15 DEGs were recognized. The GO and KEGG analyses revealed that the DEGs were enriched in the steroid metabolic process, receptor ligand activity, GnRH secretion, and neuroactive ligand-receptor interaction. The recognized DEGs were primarily implicated in cell-type benign neoplasm, kidney failure, infertility, and PE. Gene sets related to hormone activity, glycosylation, multicellular organism process, and response to BMP were activated in PE. The LEP gene was distinguished as a diagnostic biomarker of PE (AUC = 0.712) and further certified in the GSE75010 data set (AUC = 0.850). The high expression of LEP was associated with PE in clinical samples. In addition, the analysis of the immune microenvironment showed that gamma delta T cells, memory B cells, M0 macrophages, and regulatory T cells were positively correlated with LEP expression (P < 0.05). Conclusion: LEP expression can be considered to be a diagnostic biomarker of PE and can offer a novel perspective for future studies regarding the occurrence and molecular mechanisms of PE.

YTHDC1 maintains trophoblasts function by promoting degradation of m6A-modified circMPP1

N6-methyladenosine (m6A) is the most abundant mRNA internal modification in eukaryotic mRNAs. This study focuses on the effect of circMPP1 on placental villi function and the molecular mechanism. First, differentially expressed circular RNAs (circRNAs) in placenta tissues of large-for-gestational-age(LGA) neonates were screened by m6A-circRNA Epitranscriptomic Microarray and bioinformatics analyses. The abnormal expression of circMPP1 in placental tissues and cell lines was validated by RT-qPCR. In-vitro and in-vivo functional experiments were performed to evaluate the role of circMPP1 in placental impairment and fetal dysplasia. The interacting proteins of circMPP1 were identified and validated using RNA pull-down, RNA immunoprecipitation, fluorescence in situ hybridization, and immunofluorescence experiments. Protein interactions and expression levels were detected by Co-immunoprecipitation and western blot analysis. The m6A modification in circMPP1 was verified by methylated RNA immunoprecipitation assay. Bioinformatics analyses showed that circMPP1 was highly expressed in tissues with disordered placental function. In-vitro and in-vivo functional experiments showed that circMPP1 inhibited the function of placental villi. Further mechanism analyses showed that circMPP1 activated the NF-kappa B and MAPK3 signaling pathways. In addition, the m6A "reader" protein YTHDC1 was found to reduce circMPP1 expression via m6A modification. In conclusion, this study demonstrates that YTHDC1 maintains trophoblasts function by promoting degradation of m6A-mediated circMPP1.

Bioinformatics analysis combined with clinical sample screening reveals that leptin may be a biomarker of preeclampsia

Introduction: Preeclampsia (PE) is a gestational hypertensive disease with unclear pathogenesis. This study aimed to identify the genes that play an important role in determining the pathogenesis of PE using bioinformatics analysis and fundamental researches. Materials and methods: Datasets from the Gene Expression Omnibus (GEO) database were used to screen for differentially expressed genes (DEGs). The NCBI, SangerBox, and other databases were used to analyze the functions of the DEGs. Targetscan7, miRWalk, ENCORI, DIANA TOOLS, CircBank databases, and the Cytoscape tool were used to construct the lncRNA/circRNA-miRNA- LEP network. SRAMP, RPISeq, RBPsuite, and catRPAID were used to analyze the RNA modifications of LEP. Immune cell infiltration was analyzed using the dataset GSE75010. Placental tissues from normal pregnant women and PE patients were collected, screened for gene expression using reverse transcription quantitative polymerase chain reaction (RT-qPCR) and western blotting. The results were further verified in HTR-8/SVneo cell line hypoxia model and PE mouse model. Results: Our analyses revealed that LEP was significantly upregulated in eight datasets. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses indicated that LEP was involved in the JAK/STAT signaling pathway, angiogenesis, and placental development. Immune cell infiltration analysis showed that M1 and M2 macrophages differed between normal pregnancies and those in PE patients. A competing endogenous RNA (ceRNA) network was constructed, and proteins interacting with LEP were identified. RNA modification sites of LEP were also identified. Finally, the overexpression of LEP in PE was confirmed in clinical samples, HTR-8/SVneo cell line and PE mouse model. Conclusion: Our results indicate that LEP overexpression is associated with PE and may be a potential diagnostic marker and therapeutic target.

Role of osteopontin (OPN) in uterine spiral artery remodeling

Uterine spiral artery (SpA) remodeling is critical for a successful pregnancy. The deficiency of SpA remodeling seriously affects the blood perfusion of the placenta, impacting the nutritional supply to the fetus and therefore fetal growth and development, which is one of the pathological causes of pregnancy related diseases. This process involves the interaction between all cells and related factors at the maternal-fetal interface, especially extravillous trophoblast cells (EVT), vascular smooth muscle cells (VSMCs) and decidual immune cells. Osteopontin (OPN), as a glycosylated protein, is widely localized in the extracellular matrix and participates in a variety of cellular activities such as migration, adhesion, differentiation and survival. OPN plays an important role in placental development, uterine decidualization and pregnancy success. This study focuses on the role of OPN in uterine spiral artery remodeling and its related molecular mechanism.

Prediction of Differentially Expressed Genes and a Diagnostic Signature of Preeclampsia via Integrated Bioinformatics Analysis

Background

Preeclampsia (PE), which has a high incidence rate worldwide, is a potentially dangerous syndrome to pregnant women and newborns. However, the exact mechanism of its pathogenesis is still unclear. In this study, we used bioinformatics analysis to identify hub genes, establish a logistic model, and study immune cell infiltration to clarify the physiopathogenesis of PE.

Methods

We downloaded the GSE75010 and GSE10588 datasets from the GEO database and performed weighted gene coexpression network analysis (WGCNA) as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The online search tool for the retrieval of interacting genes and Cytoscape software were used to identify hub genes, which were then used to establish a logistic model. We also analyzed immune cell infiltration. Finally, we verified the expression of the genes included in the predictive model via RT-PCR.

Results

A total of 100 and 212 differently expressed genes were identified in the GSE75010 and GSE10588 datasets, respectively, and after overlapping with WGCNA results, 17 genes were identified. KEGG and GO analyses further indicated the involvement of these genes in bioprocesses, such as gonadotropin secretion, immune cell infiltration, and the SMAD and MAPK pathways. Additionally, protein-protein interaction network analysis identified 10 hub genes, six (FLT1, FLNB, FSTL3, INHA, TREM1, and SLCO4A1) of which were used to establish a logistic model for PE. RT-PCR analysis also confirmed that, except FSTL3, these genes were upregulated in PE. Our results also indicated that macrophages played the most important role in immune cell infiltration in PE.

Conclusion

This study identified 10 hub genes in PE and used 6 of them to establish a logistic model and also analyzed immune cell infiltration. These findings may enhance the understanding of PE and enable the identification of potential therapeutic targets for PE.

ceRNA Network and Functional Enrichment Analysis of Preeclampsia by Weighted Gene Coexpression Network Analysis

BACKGROUND

Preeclampsia (PE) is a multisystemic syndrome which has short- and long-term risk to mothers and children and has pluralistic etiology.

OBJECTIVE

This study is aimed at constructing a competitive endogenous RNA (ceRNA) network for pathways most related to PE using a data mining strategy based on weighted gene coexpression network analysis (WGCNA).

METHODS

We focused on pathways involving hypoxia, angiogenesis, and epithelial mesenchymal transition according to the gene set variation analysis (GSVA) scores. The gene sets of these three pathways were enriched by gene set enrichment analysis (GSEA). WGCNA was used to study the underlying molecular mechanisms of the three pathways in the pathogenesis of PE by analyzing the relationship among pathways and genes. The soft threshold power (β) and topological overlap matrix allowed us to obtain 15 modules, among which the red module was chosen for the downstream analysis. We chose 10 hub genes that satisfied ∣log2Fold Change | >2 and had a higher degree of connectivity within the module. These candidate genes were subsequently confirmed to have higher gene significance and module membership in the red module. Coexpression networks were established for the hub genes to unfold the connection between the genes in the red module and PE. Finally, ceRNA networks were constructed to further clarify the underlying molecular mechanism involved in the occurrence of PE. 56 circRNAs, 17 lncRNAs, and 20 miRNAs participated in the regulation of the hub genes. Coagulation factor II thrombin receptor (F2R) and lumican (LUM) were considered the most relevant genes, and ceRNA networks of them were constructed.

CONCLUSION

The microarray data mining process based on bioinformatics methods constructed lncRNA and miRNA networks for ten hub genes that were closely related to PE and focused on ceRNAs of F2R and LUM finally. The results of our study may provide insight into the mechanisms underlying PE occurrence.